% read imgs
negImgs = imageDatastore('neg');
posImgs = imageDatastore('pos');
% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();
noseDetector = vision.CascadeObjectDetector('Nose', 'UseROI', true);
bboxImgs=[];
for i = 1:59
img = readimage(posImgs, i);
bbox = step(faceDetector, img);
% noseBBox = step(noseDetector, img, bbox(1,:));
videoOut = insertObjectAnnotation(img,'rectangle',bbox,'Face');
figure, imshow(videoOut), title('Detected face');
bboxImgs = [bboxImgs; bbox];
end
save('step1.mat');
add address and bbox into variable
load('step1.mat');
posInstance = [];
pos = [];
negInstance = [];
for i = 1:59
% bboxImgs(i,:)
% posImgs.Files(i)
pos = [posImgs.Files(i), bboxImgs(i,:)];
posInstance = [posInstance; pos];
%negInstance = [negInstance; negImgs.Files(i)];
end
save('step2.mat');
Training
load('step2.mat');
filenames = {'imageFilename', 'objectBoundingBoxes'};
posInstance = cell2struct(posInstance, filenames, 2);
negFolder='C:\Users\chaofanz\Desktop\utasLearning\412ComputerVision\week10 face detection\neg';
% Train a cascade object detector called 'stopSignDetector.xml' using HOG features.
trainCascadeObjectDetector('kelDetector.xml',posInstance, ...
negFolder,'FalseAlarmRate',0.1,'NumCascadeStages',5,'FeatureType', 'Haar');
save('step3.mat');
Testing
% load('step3.mat');
% Use the newly trained classifier to detect a stop sign in an image.
detector = vision.CascadeObjectDetector('kelDetector.xml');
% load img
testImgs = imageDatastore('test');
for i=1:10
img = readimage(testImgs, i);
% Detect a stop sign.
bbox = step(detector,img);
% Insert bounding box rectangles and return the marked image.
detectedImg = insertObjectAnnotation(img,'rectangle',bbox,'stop sign');
% Display the detected stop sign.
figure; imshow(detectedImg);
end
